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At least 91 records · Page 5Linked to original sources
PubMed1991

Statistical model in tests for eye irritants.

A test used to classify substances for eye irritancy, as required by the Consumer Product Safety Commission, is performed on 1-3 groups of 6 albino rabbits in a sequential manner. When the statistical implications of the test are realized, it is possible for a substance to be classified as an irritant with fewer reactions than the number required for it to be classified as not an irritant. A procedure is given for correcting the inconsistency in the current test, and an alternative test, which considerably reduces the number of animals required, is proposed. Probability models and expected sample size calculations have been derived.

Animal Testing Alternatives↗
PubMed1989

Mild versus definite osteoporosis: comparison of bone densitometry techniques using different statistical models.

The purpose of this investigation was to determine the ability of three bone densitometry techniques to discriminate subjects with mild vertebral deformities from those with definite compression fractures. We determined bone mineral density (BMD) in 68 postmenopausal women by quantitative computed tomography (QCT) and dual-photon absorptiometry (DPA) of the spine, as well as single-photon absorptiometry (SPA) of the radius. Forty four individuals were classified as having mild deformities of the spine and 24 were considered to have definite vertebral compressions. Several statistical approaches were used to compare these subgroups and to estimate the relative risk of vertebral fracture. Included among these were percent decrements and zeta-scores, ROC curves, odds ratio estimations, and logistic regression analysis. Individuals with definite vertebral fractures had lower bone mineral density at all sites, but measurement of radial compact bone by SPA failed to reach significance. Using ROC analysis to distinguish mild deformities from true compressions, we found that measurement of spinal trabecular bone by QCT to be the most sensitive discriminator; although measurement of spinal integral bone by DPA also gave satisfactory discrimination, whereas assessment of radial compact bone did not adequately differentiate patients with mild deformities from those with definite compressions. Likewise, we found determination of spinal trabecular bone to be the most robust predictor of relative risk of definite fracture using either odds ratios or logistic regression analysis. Measurement of BMD in the peripheral cortical skeleton offered no predictive power for true vertebral fracture. We concluded that direct assessment of the spine, particularly of the trabecular portion, offered the strongest discrimination and relative risk prediction for definite osteoporotic fractures compared with milder forms of this condition.

Aged↗
PubMed1976

Some theory of reference values. II. Comparison of some statistical models of intraindividual variation in blood constituents.

Three models of intraindividual variation are reviewed, and statistical methods for distinguishing among them are discussed. Application of these methods to short series of observations from healthy individuals indicates that, in the large majority of cases, a strictly homeostatic model is appropriate for such constituents as serum calcium and magnesium. In less closely controlled variables, e.g., serum cholesterol and uric acid, a nonstationary, "rndom walk" model appears moresuitable in most cases. A more general autoregressive model, which includes the other models as extreme cases, could be used to describe all degrees of homeostatic control. This model is more complex, however, and requires at least 10 observations to yield estimates of acceptable precision. Moreover, it is sensitive to fluctuations in within-batch analytical variance. When biological variance is small relative to analytical variance, all three models yield essentially the same predicated values. To illustrate their use, these models have been applied to four short individual series of cholesterol observations showing increasing amounts of intrapersonal variation over long periods of time. I suggest that when less than 10 observations over time are available, the strictly homeostatic model and the nonstationary model be used to derive a "critical range" for assessing future changes. When longer series are available, the more general model might replace the other two for this purpose, if analytical variation has remained reasonably stable (within +/- 20% of its average value) during the period of observation. Much more experience with the use of all three models in health monitoring programs would be highly desirable.

Analysis of Variance↗
PubMed1988

Recognition of a category of responders to group II, slow-grower associated antigens amongst Kuwaiti senior school children, using a statistical model.

A mathematical model previously developed to test the validity of categorisation of skin test responders has been applied to data obtained from 3 age groups of Kuwaiti school children. Two specially designed sets of 4 new tuberculins were tested on senior school children to determine whether extra categories of responders might exist amongst them. Strong statistical evidence has been obtained that a proportion of the children respond to group ii, slow-grower associated antigen, creating a fourth responder category, but no evidence was found for responses to group iii, fast-grower associated antigen. The significance of group ii antigens in immune protection from tuberculosis has never been considered specifically. It is of special interest to note that responders to these antigens have been readily found in Kuwait, a country where BCG is thought to be effective, whereas no such category could be found in India or Sri Lanka, where the efficacy of the vaccine is less certain.

Adolescent↗
PubMed1985

A statistical model for species extrapolation using categorical response data.

Predictions of human health risk for single chemicals are often based on animal studies and hence require some sort of adjustment for species differences in toxic susceptibility. In the past, either the animal dose has been divided by an uncertainty factor or the dose has been transformed by a mathematical model into a human equivalent dose. A generalization of the allometric model previously used for carcinogens, the so-called "surface area model," is investigated here for use with graded severity response data for noncarcinogenic systemic toxicity. Statistical methods for estimating one of the model's parameters, the power of body weight, are proposed and tested on simulated and actual toxicity data. Early results indicate reasonable accuracy if data are available for a large number of dose groups.

Animals↗
PubMed1985

[Control of the efficacy of anti-arrhythmia drug therapy with the ambulatory electrocardiogram. Proposal for a new analytical statistical model].

Ambulatory electrocardiography is used for evaluating antiarrhythmic drug effectiveness. Statistical methods based on the analysis of the number of ventricular ectopic beats are currently employed. These techniques are not useful to compare groups of patients with different therapies, due to the wide spontaneous variability of the ectopic beats. We propose a new statistical method, based on the likelihood function. The new method has been tested both retrospectively on 102 patients treated with different antiarrhythmic drugs and prospectively on 12 patients subjected to three consecutive control ambulatory electrocardiograms and to a fourth one after treatment with propafenone. This new statistical method was found to be useful for comparing therapeutic effectiveness between groups of patients, whereas the traditional quantitative methods are to be preferred when drug effectiveness is evaluated in the single patient.

Adrenergic beta-Antagonists↗
PubMed1992

A statistical model and computer program to estimate association constants for the binding of fluorescent-labelled monoclonal antibodies to cell surface antigens and to interpret shifts in flow cytometry data resulting from alterations in gene expression.

Flow cytometry is used to obtain estimates for the distribution of fluorescent ligands bound to cell surface receptors throughout a cell sample. The equipment used provides light scattering parameters and also cell staining data in the form of dot plots and histograms of fluorescence intensities and the frequency of occurrence of particular fluorescence intensities. It is then assumed that fluorescence intensity is proportional to the number of labelled ligands bound to surface receptors. In this paper we present an outline of a statistical theory to account for the stretching and translation of such flow cytometry profiles which occur either as a result of alterations in gene expression, or from changing the sub-saturating concentration of fluorescent-labelled monoclonal antibodies or lectins used to stain the cells. We describe how the theory has been incorporated into two programs CSAFIT (cell surface antigen fit) and MAKCSA (make data to test CSAFIT). The program CSAFIT can be used to estimate two parameters, alpha and beta, by constrained non-linear regression analysis of the flow cytometry profiles. If the shift results from changes in the concentration of a staining agent then the estimates alpha and beta calculated by CSAFIT are functions of the ligand concentration, the ligand type and the cell line characteristics. They quantify the stretch and translation events that are encountered in flow cytometry. So when the parameter estimates alpha and beta are then further analysed as functions of ligand concentration, estimates for the average association constant K for the binding-site/ligand interaction can be obtained. This paper describes details of the development of programs CSAFIT and MAKCSA. We also discuss the distribution of parameter estimates calculated by CSAFIT and the overall performance of CSAFIT as assessed by simulation studies using data generated by MAKCSA.

Antibodies, Monoclonal↗
PubMed1990

Statistical modeling of the effects of drug combinations.

A method is described for identifying and quantitating departures from additivity (i.e., synergism and antagonism) when drugs having like effects are given in combination. It is applicable for both graded and quantal (e.g., after probit or logit transformation) responses. Log(dose)-response curves of both drugs should be linear but need not be parallel. The following model is fitted to dose-response data for both the individual drugs and combinations of drugs: Y = beta 0 + beta 1 log(A + P.B + beta 4(A.P.B)1/2) where Y is the response, A is the amount of drug A, B is the amount of drug B, and P is a relative potency of the drugs given by log(P) = beta 2 + beta 3 log(B'), in which B' is the solution to B' - B - A/P = 0. If log(dose)-response curves of the two drugs are parallel, beta 3 = 0, and P becomes a constant parameter to be estimated. A positive value of beta 4 corresponds to synergism and a negative value to antagonism. Hypothesis tests may be carried out to determine whether beta 4 is significantly different from zero.

Dose-Response Relationship, Drug↗
PubMed1990

A note on the use of statistical models in epidemiologic research on illicit drug use.

This essay aims to stimulate thinking or to remind readers about the shortcomings of standardized regression coefficients and related statistical measures in epidemiologic research on illicit drug use. This is accomplished primarily with a set of examples based on simulated epidemiologic data in which the standardized regression coefficient is shown to co-vary dramatically with frequency of the outcome variable. The basic thrust of this critique of commonly used regression models is not new; it has appeared elsewhere several times. Nevertheless, in epidemiologic research on illicit drug use, there is a continuing use of standardized regression coefficients and other margin-sensitive statistical measures without comment on their shortcomings. Thus, a specific critique with illustrations might have value.

Adolescent↗
PubMed1992

[Statistic modeling of geographic and epidemiologic variations].

In epidemiology, studies of the geographical variations of mortality or incidence rates for some chronic diseases have often given rise to etiological clues concerning those diseases. In this framework the variables concerned have a spatially autocorrelated structure which has to be taken into account in the statistical analysis. The statistical techniques used to study in the first place the spatial variations of mortality rates and then the joint geographical variations of mortality and exposure indices are reviewed. Emphasis is placed on the importance played by the geographical scale of the analysed data in the modelling process as well as on the interpretation problems of geographical correlation studies.

Bias↗
PubMed1992

Multi-level statistical models in studies of periodontal diseases.

Periodontal data typically have a hierarchical structure, with sites grouped within individuals, and individuals grouped within communities. Also, the occasion may be regarded as another level since the acquired knowledge indicates that periodontal disease activity may vary over time. Conventional statistical tests are based on unilevel analysis of data. However, this approach to statistical analysis is often inconvenient in periodontal research because of the variation in the outcome variables between the various levels in the hierarchy. Lately there have been important developments in the statistical theory which have made available powerful statistical techniques for analyzing multilevel or hierarchical data. This report describes a new approach for analyzing periodontal data and uses an illustrative example to build a model which explains part of the variability in the response variable. The results from this analysis are then compared to results from an earlier report which uses unilevel methods and the findings discussed. The present multilevel approach has several advantages over unilevel methods, mainly due to its statistical validity and efficiency. Further, it permits the incorporation of explanatory variables measured at the site and the subject levels, and those which vary across the time points. Multilevel analyses have a promising potential and are expected to have a significant impact on periodontal research.

Age Factors↗